Papers with social media data
Investigating label suggestions for opinion mining in German Covid-19 social media (2021.acl-long)
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| Challenge: | Existing difficulties in data annotation are due to prolonged data gathering processes or opinion surveys being subject to reactivity. |
| Approach: | They propose to use label suggestions to improve annotation efficiency in german Covid-19 data by providing annotators with pre-recorded annotations. |
| Outcome: | The proposed model improves inter-annotator agreement and annotation quality in a controlled study with social science students. |
An In-depth Analysis of the Effect of Lexical Normalization on the Dependency Parsing of Social Media (D19-55)
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| Challenge: | Existing natural language processing tools are focused on standard texts, but performance drops when used on a different domain. |
| Approach: | They analyze the effect of manual and automatic lexical normalization for dependency parsing . they conclude that automatic normalization scores close to manually annotated normalization . |
| Outcome: | The proposed approach improves performance on social media data for many tasks . it is unclear which replacements have the most impact and what weaknesses exist in the system . |
Hybrid Emoji-Based Masked Language Models for Zero-Shot Abusive Language Detection (2020.findings-emnlp)
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| Challenge: | Recent studies have demonstrated the effectiveness of cross-lingual language model pre-training on NLP tasks. |
| Approach: | They propose a hybrid emoji-based Masked Language Model to leverage eojis across languages to improve the learning of short text messages. |
| Outcome: | The proposed model performs better on German, Italian and Spanish. |
Detecting de minimis Code-Switching in Historical German Books (2020.coling-main)
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| Challenge: | Code-switching has drawn scholarly attention in computational linguistics and natural language processing from many different perspectives. |
| Approach: | They propose to compare informal code-switching to its appearance in more formal registers by annotating and inspecting the German textarchives. |
| Outcome: | The proposed classifiers can help reduce errors when speech recognition is applied to a large corpus with rare embedded languages. |
Revealing COVID-19’s Social Dynamics: Diachronic Semantic Analysis of Vaccine and Symptom Discourse on Twitter (2024.findings-emnlp)
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| Challenge: | Social media data provide a new source for social science and cultural analysis research, but its analysis is challenging due to the semantic shift phenomenon, where word meanings evolve over time. |
| Approach: | They propose an unsupervised dynamic word embedding method to capture longitudinal semantic shifts in social media data without predefined anchor words. |
| Outcome: | The proposed method captures longitudinal semantic shifts in social media data without predefined anchor words and leverages word co-occurrence statistics and dynamic updating to adapt embeddings over time. |
Real-time Change Point Detection using On-line Topic Models (C18-1)
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| Challenge: | Existing methods for detecting events from publicly available data streams such as twitter have been used to model topics from large corpora. |
| Approach: | They propose to use on-line Latent Dirichlet Allocation to model topic shifts and on-lines change point detection algorithms to detect when significant changes occur. |
| Outcome: | The proposed algorithm yields F-scores up to 52% on the detection of real-life changes from social media data streams. |
Urban Dictionary Embeddings for Slang NLP Applications (2020.lrec-1)
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| Challenge: | a new set of word embeddings is released to improve word embedment performance . word embeds provide useful representations of meanings of words in vectors . |
| Approach: | They present a set of word embeddings trained on Urban Dictionary . they show they have high performance across a range of common word embeding evaluations . |
| Outcome: | The first set of word embeddings trained on Urban Dictionary has high performance . the embeddables perform better on a range of common word evaluation tasks . |
Geographically-Informed Language Identification (2024.lrec-main)
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| Challenge: | a paper develops a method to identify languages based on geographic origin of text . the model is based in regions where languages are widely spoken and may occur anywhere . |
| Approach: | They propose to incorporate geographic information into a language identification model to ensure coverage of linguae francae regardless of location. |
| Outcome: | The proposed model includes 31 widely-spoken international languages . the model improves on social media data and improves performance on 916 languages compared to baseline models . |
Enhancing Data Quality through Simple De-duplication: Navigating Responsible Computational Social Science Research (2024.emnlp-main)
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| Challenge: | Social media data exhibits distinctive characteristics such as rapid and continual topic evolution. |
| Approach: | They propose new protocols and best practices for improving dataset development from social media data and its usage. |
| Outcome: | The proposed protocols and best practices improve the performance of social media datasets and their usage. |
Norm It! Lexical Normalization for Italian and Its Downstream Effects for Dependency Parsing (2020.lrec-1)
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| Challenge: | Existing tools for lexical normalization of social media data are designed with canonical texts in mind, and this makes it difficult to process data in multiple languages. |
| Approach: | They propose to create a lexical normalization dataset for Italian and analyze the inter-annotator agreement for this task. |
| Outcome: | The proposed model improves the parsing of social media data in Italian and shows that it can be used to translate non-standard social media content to canonical language. |